A <i>FLOT1</i> host regulatory allele is associated with a recently expanded <i>Mtb</i> clade in patients with tuberculosis

Luo, Yang; Huang, Chuan-Chin; Liu, Qingyun; Howard, Nicole; Zhu, Junhao; Amariuta, Tiffany; Asgari, Samira; Ishigaki, Kazuyoshi; Calderón, Róger; Moody, D. Branch; Lecca, Leonid; Fortune, Sarah M.; Murray, Megan; Raychaudhuri, Soumya

doi:10.1101/2022.02.07.22270622

Cited by 4 publications

(4 citation statements)

References 63 publications

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“…We describe the first TB genome-to-genome (G2G) study conducted in an African population. We identified two pairs of associated genetic loci that were not identified in previous G2G studies or GxG interaction studies [34][35][36][37][38] . In addition, we show that although variations in M.tb epitopes are rare, certain M.tb epitope variants are associated with human HLA variation, a sign of probable immune selective pressure.…”

Section: Discussionmentioning

confidence: 96%

Genome-to-genome analysis reveals associations between human and mycobacterial genetic variation in tuberculosis patients from Tanzania

Zwyer

Brites

et al. 2023

Preprint

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The risk and prognosis of tuberculosis (TB) are affected by both human and bacterial genetic factors. To identify interacting human and bacterial genetic loci, we leveraged paired human andMycobacterium tuberculosis(M.tb) genomic data from 1000 Tanzanian TB patients. Through a genome-to-genome approach, we identified two pairs of human andM.tbgenetic variants that are significantly associated. One of the human genetic variants maps to the intron ofPRDM15, a gene involved in apoptosis regulation. The other human variant maps to an intergenic region close toTIMM21andFBXO15. In addition, we observed that a group of linkedM.tbepitope variants were significantly associated with HLA-DRB1 variation. This suggests that even though epitope variation is rare inM.tbin general, specific epitopes might still be under immune selective pressure. Overall, our study pinpoints sites of genomic conflicts between humans andM.tb, suggesting bacterial escape from host selection pressure.

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Section: Discussionmentioning

confidence: 96%

Genome-to-genome analysis reveals associations between human and mycobacterial genetic variation in tuberculosis patients from Tanzania

Zwyer

Brites

et al. 2023

Preprint

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“…With the increasing availability of bacterial WGS data, it is now possible to use GWAS to probe the relationship between pathogen genotypes and clinical disease phenotypes. GWAS has identified genetic determinants of MTB drug resistance ( Coll et al, 2018 ; Farhat et al, 2013 ), TB transmission ( Sobkowiak et al, 2020 ), virulence ( Genestet et al, 2022 ) and host preference ( Luo et al, 2022 ). In this study, GWAS was used to identify bacterial genetic variants associated with poor treatment outcomes in patients with drug-susceptible TB and assess their impact on outcomes.…”

Section: Discussionmentioning

confidence: 99%

The mutational signatures of poor treatment outcomes on the drug-susceptible Mycobacterium tuberculosis genome

Chen

Jiang

Mijiti

et al. 2023

eLife

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Drug resistance is a known risk factor for poor tuberculosis (TB) treatment outcomes, but the contribution of other bacterial factors to poor outcomes in drug susceptible TB is less well understood. Here, we generate a population-based dataset of drug-susceptible Mycobacterium tuberculosis (MTB) isolates from China to identify factors associated with poor treatment outcomes. We analyzed whole-genome sequencing (WGS) data of MTB strains from 3196 patients, including 3105 patients with good and 91 patients with poor treatment outcomes, and linked genomes to patient epidemiological data. A genome-wide association study (GWAS) was performed to identify bacterial genomic variants associated with poor outcomes. Risk factors identified by logistic regression analysis were used in clinical models to predict treatment outcomes. GWAS identified fourteen MTB fixed mutations associated with poor treatment outcomes, but only 24.2% (22/91) of strains from patients with poor outcomes carried at least one of these mutations. Isolates from patients with poor outcomes showed a higher ratio of reactive oxygen species (ROS)-associated mutations compared to isolates from patients with good outcomes (26.3% vs 22.9%, t test, P=0.027). Patient age, sex, and duration of diagnostic delay were also independently associated with poor outcomes. Bacterial factors alone had poor power to predict poor outcomes with an AUC of 0.58. The AUC with host factors alone was 0.70, but increased significantly to 0.74 (DeLong's test, P = 0.01) when bacterial factors were also included. In conclusion, although we identified MTB genomic mutations that are significantly associated with poor treatment outcomes in drug-susceptible TB cases, their effects appear to be limited.

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“…With the increasing availability of bacterial WGS data has made it possible to use GWAS to study the relationship between pathogen genotypes with clinical disease phenotypes. In MTB, GWAS has identified genetic determinants of drug resistance (Coll et al, 2018; Farhat et al, 2013), TB transmission (Sobkowiak et al, 2020), host preference (Luo et al, 2022) and virulence (Genestet et al, 2022). In this study, GWAS analysis was used to identify bacterial genetic variants associated with poor outcomes in drug-susceptible TB patients, and assess their impact.…”

Section: Discussionmentioning

confidence: 99%

The mutational signatures of poor treatment outcomes on the drug-susceptibleMycobacterium tuberculosisgenome

Chen

Jing

Mijiti

et al. 2022

Preprint

View full text Add to dashboard Cite

Drug resistance is a known risk factor for poor tuberculosis (TB) treatment outcomes, but the contribution of other bacterial factors to poor outcomes in drug susceptible TB is less understood. Here, we generate a population-based dataset of drug-susceptibleMycobacterium tuberculosis(MTB) isolates from China to identify factors associated with poor treatment outcomes. We sequenced the whole genome of 3496 MTB strains and linked genomes to patient epidemiological data. A genome-wide association study (GWAS) was performed to identify bacterial genomic variants associated with poor outcomes. Risk factors identified by logistic regression analysis were used in clinical models to predict treatment outcomes and their associations were assessed with structural equation models (SEM). GWAS identified fourteen MTB variants (24.2% vs 7.5%, P<0.001) and ade novoreactive oxygen species (ROS) mutational signature (26.3%±18.2% vs 22.9%±13.8%, P=0.027) that were more frequent in patients with poor treatment outcomes. Patient age, sex, and duration of diagnostic delay were also independently associated with poor outcomes. The best clinical prediction model, with an AUC of 0.74, incorporates both host and bacterial risk factors, and host factors are more important. Together, our results reveal that although host factors are the most important determinants for poor treatment outcomes, the genomic characteristics of the infecting MTB strain may also contribute significantly to poor treatment outcomes. Fourteen genetic variants were statistically associated with poor TB treatment outcomes, but the optimal model for predicting treatment outcomes includes both patient characteristics and bacterial genomic determinants.

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A FLOT1 host regulatory allele is associated with a recently expanded Mtb clade in patients with tuberculosis

Cited by 4 publications

References 63 publications

Genome-to-genome analysis reveals associations between human and mycobacterial genetic variation in tuberculosis patients from Tanzania

Genome-to-genome analysis reveals associations between human and mycobacterial genetic variation in tuberculosis patients from Tanzania

The mutational signatures of poor treatment outcomes on the drug-susceptible Mycobacterium tuberculosis genome

The mutational signatures of poor treatment outcomes on the drug-susceptibleMycobacterium tuberculosisgenome

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